In all experiments below, \(q_{\phi }\)  is a normalizing flow [1]} with an autoregressive architecture implemented via the masked autoencoder for distribution estimation (MADE) [2]}. We follow the same setup from [3]} and [4]} for LFI problems, stackings five MADEs, each with two hidden layers of 50 units, and a standard normal base distribution for the normalizing flow. This choice provides a sufficiently flexible function capable of approximating complex posterior distributions. We refer the reader to [5]} for more information on the different types of normalizing flows. We run SNPE-C with \(R = 10\)  rounds and \(N = 5000\)  simulations per round.
